Results for “field optimization” — Page 20

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRSDate not supplied

Real World Applications of AI/ML in Optimizing Airspace Operations

As National Airspace System (NAS) is going through the Digital Transformation journey, data science and analytics methods can significantly contribute to improving the traditional physics-based decision-making tools. The adoption of AI/ML methods will not only help accelerate Federal Aviation Administration (FAA)’s vision of Info-centric NAS but also contribute to the overall objective of sustainable aviation. AI/ML can improve the ground and airspace operations by enhancing the accuracy of current decision-making tools used by the airlines and the FAA to manage traffic on the ground and in the air. Huge amount of data that gets collected during a flight. AI/ML methods can extract information from this data and provide valuable insights to make better operational decisions. NASA has partnered with the FAA and commercial airlines such as American and Southwest Airlines on this effort and has successfully demonstrated the benefits of using ML in real world environment by reducing delays and optimizing ground operations at the US airports. In 2022 itself, NASA demonstrated real-world benefits (over 24K lbs. of fuel savings, over 76.6K lbs. CO2 emission savings, and several hours of delay savings) by deploying ML based prediction models to optimize ground operations at Dallas/Fort Worth International and Dallas Love Field Airports in Texas. These tools are being deployed on the cloud for broader deployment, adaptability, and scalability. NASA is developed a reference implementation of the cloud-based platform to significantly lower the bar to development and distribution of these digital services for aviation. In this talk, I will share information about the Digital Information Platform project, the novel AI/ML based approaches used for optimizing ground operations and the opportunities to partner with NASA on these demonstrations.

air traffic management↗

NASA NTRS2018

Far-Field Pattern Measurement and Simulation of VHF Antenna at 60 MHz for Europa Clipper Mission

This paper presents measurements and simulations of a linearly polarized VHF folded dipole operating at 60 MHz for NASA’s upcoming Europa Clipper mission. The spacecraft contains a ground penetrating radar, consisting of an array of four VHF antennas, that will characterize the surface of Jupiter’s icy moon Europa. Measured far-field radiation patterns and reflection coefficients above a 50×80m perfect ground plane are compared to predicted results using commercial software. Normalized Far-Field radiation pattern cuts are measured utilizing a biconical antenna and receiver mounted on a flying drone. Simulations of the radiation pattern show that the number of lobes increases with the distance of the antenna from the ground due to the ground-bounce phenomenon. In addition, the measured and simulated reflection coefficients stay unchanged for height above 2 meters, which matches the predicted calculation of the reactive-near field region of this antenna. However, the antenna electrical parameters are optimized for use on the spacecraft and become detuned in the presence of a ground plane. Simulated and measured return loss discrepancy less than 0.5 dB is presented. Radiation pattern comparisons show similar agreement when taking into account the effect of far-field measurement uncertainties.

Decrossas, Emmanuel↗

NASA NTRS2020

High-Resolution Mapping of SO2 Using Airborne Observations from the GeoTASO Instrument During the KORUS-AQ Field Study: PCA-Based Vertical Column Retrievals

The Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) instrument is an airborne hyperspectral spectrometer measuring backscattered solar radiation in the ultraviolet (290–400 nm) and visible (415–695 nm) wavelength regions. This paper presents high-resolution sulfur dioxide (SO2) maps over the Korean Peninsula, produced by SO2 retrievals from GeoTASO measurements during the Korea–United States Air Quality Field Study (KORUS-AQ) from May to June 2016. The highly sensitive GeoTASO instrument with a spatial resolution of ~250 m × 250 m can detect point emission sources of SO2 within its fields of view, even without merging multiple overlapping observations. To retrieve SO2 vertical columns from the GeoTASO measurements, we apply an algorithm based on principal component analysis (PCA), which is effective in suppressing noise and biases in SO2 retrievals. The retrievals successfully capture SO2 plumes and various point sources such as power plants, a petrochemical complex, and a steel mill, located in South Chungcheong Province, some of which are not detected by a ground-based in situ measurement network. Spatial distributions of SO2 from GeoTASO observations in source areas are consistent with those from the Stack Tele-Monitoring System reports and airborne in situ SO2 measurements. Comparisons of SO2 retrievals from GeoTASO and existing satellite sensors demonstrate the significance of high-resolution SO2 observations, by indicating that GeoTASO detects small SO2 emission sources that are not precisely resolved by single overpasses of satellites. To assess future geostationary SO2 observations having a higher spatial resolution, we upscale the GeoTASO SO2 retrievals to a spatial resolution of the Geostationary Environment Monitoring Spectrometer (GEMS). Since the upscaled GeoTASO retrievals also detect SO2 plumes clearly, we expect from GEMS to identify even small SO2 emission sources over Asia.

Chong, Heesung↗

NASA NTRS2001

STOP Analysis and Optimization of a Very-Low-Distortion Space Instrument: HST WFC3 Case Study

New generation optical instruments with very demanding stability requirements are being proposed and developed for space applications. STOP (Structural-Thermal-Optical Performance) analysis and optimization is crucial in meeting the very tight distortion budgets of these instruments. This presentation outlines STOP analysis and optimization approach in the context of WFC3 (Wide-Field Camera 3), which is a radial instrument designed to replace the Wide-Field Planetary Camera 2 (WFPC2) of the Hubble Space Telescope (HST). WFC3 houses two separate channels, UVIS and IR, and will have greater throughput and sensitivity than WFPC2. WFC3 line-of-sight alignment budget for the UVIS and IR channels are as small as 10 and 20 milli-arcsec, respectively. Its optical bench is the most critical subsystem effecting the optical stability of WFC3 hence our effort concentrates on the design and analysis of the bench and its interfaces. Structural analysis has accompanied the mechanical design of the bench since the initial concept study. A high fidelity structural Finite Element Model (FEM) of the bench has been developed and used for minimizing its thermally induced distortions as well as sizing it to meet the stiffness and strength requirements of a Shuttle launch. The bench is a composite honeycomb panel box structure with a very low planar Coefficient of Thermal Expansion (CTE) of approximately 0.1 ppm/C. Optic components are mounted to super-INVAR inserts bonded into the panels. The bench is kinematically supported on three HST latches via interface struts, which are tailored to exhibit negative CTE to cancel out the thermal motions of the latches. The interface struts also incorporate flexure elements to minimize the mechanical distortions coming into the bench from its enclosure. Bench FEM is coupled with the enclosure FEM to quantify these effects. Short term or on-orbit STOP analysis includes distortion due to the temperature variations of the bench, the struts, and the enclosure. Long term or ground-to-orbit STOP analysis includes distortional effects of gravity release, desorption, and assembly in addition to the ground-to-orbit temperature variations. A rigorous testing program has been implemented for verifying the material properties and the analysis predictions. STOP analysis results demonstrate that both the short-term and the long-term alignment budgets will be met. Presentation will cover design and analysis details that are critical to a successful implementation of the STOP analysis and optimization process.

Kunt, Cengiz↗

NASA NTRS2021

Aircraft Flaps Modeling in OpenMDAO

The goal of this project was to develop a model for a single subsystem in the aerodynamics discipline, in this case the flaps of an aircraft. Flaps are high-lift devices used by planes to allow for quicker takeoffs, and slower landings. A computer-based aircraft model of the flaps of an aircraft was developed using the Python based open-source framework OpenMDAO. OpenMDAO is used to develop multi-disciplinary aircraft models using gradient-based optimization; design optimization (MDO) is concerned with solving design problems involving numerical models of complex engineering systems. There were 4 components in the model; each has input values, output variables, and equations to calculate said outputs. The variables and equations are sourced from NASA Fortran code from the 1970s, in a project called the General Aviation Synthesis Program (GASP). These variables and equations which create the model are being converted to Python for ease of use. The flaps model developed will be integrated into a larger model of a conventional aircraft’s flight phases. All subsystems of the model will first be built using the parameters of a Boeing 737 MAX-8, to validate its functionality and accuracy. Then, the aircraft model will be used for hybrid-electric research; running optimizations to improve efficiency, minimize fuel burn, and advance hybrid-electric technology in the aerospace field.

Computer-based aircraft modeling↗

NASA NTRS2019

NIRCam: The JWST Near-IR Camera and Slitless Spectrograph

NIRCam is the primary near-infrared camera for JWST. It is well-suited for a wide variety of star formation and other scientific studies and will also be used for aligning the JWST primary mirror segments and telescope. NIRCam will provide high sensitivity, high spatial resolution images in a variety of filters over the 0.6 - 5 μm wavelength region including coronagraphic modes and slitless spectroscopic capabilities. NIRCam consists of two identical optical modules that have adjacent 2.2' x 2.2' fields of view on the sky. Each module is composed of two channels that observe the same field at short (< 2.4 μm) or long (> 2.4 μm) wavelengths simultaneously, with optimized spatial sampling in each channel. NIRCam optical elements include narrow (R = 100), medium (R = 10), and wide (R <= 4) filters, coronagraphic imaging spot and bar masks, and grisms that provide wide-field or time-series slitless spectroscopy. These major NIRCam features will be introduced, and some applications to galactic star formation will be briefly presented.

Greene, Thomas↗

NASA NTRS2020

Communication-Aware Orbit Design for Small Spacecraft Swarms around Small Bodies

Exploration of small Solar System bodies has traditionally been performed by single monolithic spacecraft carrying a number of science instruments. However, science instruments typically cannot be operated simultaneously due to the instrument requirements including optimal viewing angle, surface illumination, altitude and ground resolution, power, and data constraints. This observation has motivated interest in multi-spacecraft architectures where a swarm of small spacecraft, each carrying a single science instrument, studies a small body after being deployed by a carrier spacecraft, which then collects data from the vehicles and relays it to Earth. Such architectures hold promise to yield significant improvements in mission efficiency, increases in data quality, and shorter mission duration. A key difficulty in the design of such missions is the selection of orbits for the small spacecraft, which must satisfy not only instrument requirements, but also strict inter-spacecraft communication and on-board storage constraints. To address this, in this paper, we present a novel computationally-efficient optimization algorithm for \emph{communication-aware design} of the orbits of a small spacecraft swarm orbiting a small body. The proposed approach captures constraints including instrument requirements, inter-spacecraft communication bandwidths, and on-board memory usage, and it can accommodate highly irregular gravity field models and surface geometries. We propose an efficient algorithm for optimization of instrument observations and inter-spacecraft communications; we then leverage the differentiable nature of the proposed algorithm to accelerate a gradient-based global search algorithm. Numerical simulations of a six-spacecraft swarm studying 433 Eros show that the proposed approach successfully identifies high-quality orbits, and significantly outperform communication-agnostic optimization techniques, resulting in a 10% increase in scientific returns and a 30% increase in the quality of the collected data.

Rahmani, Amir↗

NASA NTRS2022

How to Build a Rover: An Overview of the Mars 2020 Mission’s Vehicle System Testbed

While NASA’s Mars rover Perseverance continues to make groundbreaking achievements on the Red Planet, its twin is hard at work here on Earth. The Operational Perseverance Twin for the Integration of Mechanisms and Instruments Sent to Mars, or OPTIMISM, is the Mars 2020 Vehicle System Testbed (VSTB) rover operated by NASA Jet Propulsion Laboratory (JPL) in Pasadena, California. OPTIMISM’s home is the JPL Mars Yard; an outdoor field with red soil that simulates the terrain encountered by Perseverance. The VSTB is a full-scale engineering model of the flight rover, serving a number of functions to ensure mission operations can continue smoothly and on schedule. The VSTB possesses instrumentation, computers, mechanisms, cameras, and a Mobility subsystem that are nearly identical to its extraterrestrial twin. Its high fidelity allows the rover to be a highly effective tool to fully test system functionality and performance prior to commanding the flight rover. The early stages of building OPTIMISM began a few months prior to Perseverance departing JPL for Cape Canaveral, FL in early 2020. Electrical integration of the flight system avionics, and compatibility checkouts of the electrical ground support equipment ensured that the foundation of the electrical system was operational and in place. Next, the internal harnessing was installed and compatibility checks of the rover instrumentation and mechanisms were performed to confirm the system was prepared for full buildup. Finally, mechanical assembly of the rover chassis with its external components completed the integration of the system before it was moved to the Mars Yard for its initial phase of testing to perform verification & validation (V&V) of the Mobility subsystem requirements. By the time Perseverance landed at Jezero Crater in February 2021, the first phase of VSTB operations was underway. Surface guidance, navigation, and control (SGNC) testing for the Mobility subsystem ensured functionality and performance requirements were met for various capabilities such as visual odometry (VO), mapping, and automatic navigation (AutoNav). Subsequent integration of the robotic arm (RA) onto the VSTB enabled the V&V campaign for surface sampling operations (SSO) to commence. As the mission’s engineering operations (EO) have gotten underway, the VSTB has been utilized for an array of purposes including troubleshooting software anomalies, and performing dry-runs for first time activities (FTAs) prior to sending the commands to Perseverance. OPTIMISM will continue to serve mission critical functions as long as Perseverance is roving the Red Planet.

Rojas, Jose Trujillo↗

NASA NTRS1975

High efficiency graded band-gap Al/x/Ga/1-x/As-GaAs p-on-n solar cell

A theoretical analysis of p-on-n (p/n) graded band-gap Al/x/Ga/1-x/As-GaAs solar cells including all practical energy loss mechanisms predicts air mass zero efficiencies of 17.3%. The energy losses include those due to spectral reflection, surface, bulk and junction recombination currents, and series resistance. The device consists of a layer of p-type Al/x/Ga/1-x/As 3.5-micron thick on top of an n-type GaAs substrate. The graded band-gap is achieved by decreasing x from 0.35 at the surface to zero at the junction. The same structure without the graded Al concentration, no longer an optimum device, has an efficiency of 10.4%. The primary function of the graded band-gap material is the reduction of surface and bulk recombination losses in the surface layer by a built-in electric field. A comparison of the p/n structure with an optimized n/p structure indicates that the latter has a slightly higher efficiency (17.7%) for assumed minority carrier diffusion lengths and surface recombination velocity.

Hutchby, J. A.↗

NASA NTRS1977

Automated fabrication of back surface field silicon solar cells with screen printed wraparound contacts

The development of a process for fabricating 2 x 4 cm back surface field silicon solar cells having screen printed wraparound contacts is described. This process was specifically designed to be amenable for incorporation into the automated nonvacuum production line. Techniques were developed to permit the use of screen printing for producing improved back surface field structures, wraparound dielectric layers, and wraparound contacts. The optimized process sequence was then used to produce 1852 finished cells. Tests indicated an average conversion efficiency of 11% at AMO and 28 C, with an average degradation of maximum power output of 1.5% after boiling water immersion or thermal shock cycling. Contact adherence was satisfactory after these tests, as well as long term storage at high temperature and high humidity.

Thornhill, J. W.↗

NASA NTRS1980

High voltage spark carbon fiber detection system

The pulse discharge technique was used to determine the length and density of carbon fibers released from fiber composite materials during a fire or aircraft accident. Specifications are given for the system which uses the ability of a carbon fiber to initiate spark discharge across a high voltage biased grid to achieve accurate counting and sizing of fibers. The design of the system was optimized, and prototype hardware proved satisfactory in laboratory and field tests.

Yang, L. C.↗

NASA NTRS1980

Electromagnetic deep-probing (100-1000 KMS) of the Earth's interior from artificial satellites: Constraints on the regional emplacement of crustal resources

The applicability of electromagnetic deep sounding experiments using natural sources in the magnetosphere by incorporating Magsat data with other geophysical data was evaluated. Magsat satellite data, ground based magnetic observations, appropriate reference field models, and other satellite data was analyzed. The optimal combination of observations which lead first to a global and then to a regional characterization of the conductivity of the Earth's upper mantle is sought.

Hermance, J. F.↗

NASA NTRS1981

An operations manual for the Spinning Mode Synthesizer in the Langley Aircraft Noise Reduction Laboratory

The need for a dependable and controllable noise source and the consequent development of the Spinning Mode Synthesizer (SMS) is discussed. Configuration of the SMS incorporated into the flow duct facility is reported. Turbofan noise is composed of a series of fundamental acoustical modes, which are produced by acoustic drivers equispaced circumferentially around the flow duct. Pressure field is compared to an ideal result in an optimization algorithm, adjusting driver settings until system error is minimized. The following items are included: operating instructions, a detailed description of the system, and a user's guide to data acquisition packages available.

Palumbo, D. L.↗

NASA NTRS1985

Determination of the mobility profile in GaAs-MESFETs

A process for measuring charge carrier mobility for gallium-arsenide metal semiconductor field effect transistors is described in an attempt to optimize the relationship between this factor and production. The measuring procedure allows an actual determination of local mobility in the channel. The physical basis for the process and features of the measuring room are outlined. The measuring technique is described and recommendations are made for setting measuring parameters.

Prost, W.↗

NASA NTRS1989

Analysis of lightning field changes during active Florida thunderstorms

A computer algorithm for deriving accurate values of lightning-caused changes in cloud electric fields under active storm conditions was developed and applied to data obtained during two thunderstorms from a network of ground-based electric field mills at the NASA Kennedy Space Center and the Cape Canaveral Air Force Station. The resulting field changes were analyzed using a least-squares optimization procedure and point-charge (Q) and point-dipole (P) models. The values and the time variations of the Q-model parameters under active storm conditions were found to be similar to those reported by Maier and Krider (1986) for small storms, when the computations were carried out with the same analysis criteria and comparable biases. The parameters of the P solutions were found to vary with time within the storm interval and from storm to storm.

Koshak, William J.↗

NASA NTRS1991

Commercial Capaciflector

A capacitive proximity/tactile sensor with unique performance capabilities ('capaciflector' or capacitive reflector) is being developed by NASA/Goddard Space Flight Center (GSFC) for use on robots and payloads in space in the interests of safety, efficiency, and ease of operation. Specifically, this sensor will permit robots and their attached payloads to avoid collisions in space with humans and other objects and to dock these payloads in a cluttered environment. The sensor is simple, robust, and inexpensive to manufacture with obvious and recognized commercial possibilities. Accordingly, NASA/GSFC, in conjunction with industry, is embarking on an effort to 'spin' this technology off into the private sector. This effort includes prototypes aimed at commercial applications. The principles of operation of these prototypes are described along with hardware, software, modelling, and test results. The hardware description includes both the physical sensor in terms of a flexible printed circuit board and the electronic circuitry. The software description will include filtering and detection techniques. The modelling will involve finite element electric field analysis and will underline techniques used for design optimization.

Vranish, John M.↗